Title :
Torque Ripple Reduction in Switched Reluctance Motor Drives Using B-Spline Neural Networks
Author :
Lin, Zhengyu ; Reay, Donald S. ; Williams, Barry W. ; He, Xiangning
Author_Institution :
Dept. of Electr., Electron. & Comput. Eng., Heriot-Watt Univ., Edinburgh
Abstract :
A switched reluctance motor torque ripple reduction scheme using a B-spline neural network (BSNN) is presented. Closed-loop torque control can be implemented using an on-line torque estimator. Due to the local weight updating algorithm used for the BSNN, an appropriate phase current profile for torque ripple reduction can be obtained on-line in real time. It has good dynamic performance with respect to changes in torque demand. The scheme does not require high-bandwidth current controllers. Simulation and experimental results demonstrate the validity of the scheme
Keywords :
closed loop systems; electric machine analysis computing; machine control; neural nets; reluctance motor drives; splines (mathematics); torque; torque control; closed-loop torque control; local weight updating algorithm; online torque estimator; switched reluctance motor drives; torque ripple reduction; Commutation; Helium; Industry Applications Society; Motor drives; Neural networks; Reluctance machines; Reluctance motors; Spline; Torque control; Torque measurement; Neural networks; reluctance motors; torque control;
Journal_Title :
Industry Applications, IEEE Transactions on
DOI :
10.1109/TIA.2006.882671
